FINAL-Bench Quantum Leaderboard
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Neutral quantum-method benchmark — QEC decoders & more
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FINAL-Bench/POCKET-Image-StudioOrganizations
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🖼️ POCKET-Image — the POCKET series goes visual: character-perfect text in any language, on-device
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text.
Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe.
What it is:
• 100% accurate text, any language — where global models produce gibberish
• Any background from a prompt — text is optional (empty → a pure image)
• No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine
• Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+)
• Windows · macOS · Linux · fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
🎨 Studio — generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage
📚 The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text.
Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe.
What it is:
• 100% accurate text, any language — where global models produce gibberish
• Any background from a prompt — text is optional (empty → a pure image)
• No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine
• Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+)
• Windows · macOS · Linux · fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
🎨 Studio — generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage
📚 The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
liked 2
models 6 days ago
reacted to SeaWolf-AI's post with 🔥 6 days ago
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3107
POCKET now speaks Gemma 4 — a 26B model that loads in every app, and runs on your PC with no GPU
We're adding a Gemma-4 sibling to POCKET: POCKET-26B, built from Google's Gemma-4-26B-A4B (Apache-2.0). Our flagship POCKET-35B is a Qwen-family MoE and needs a recent llama.cpp; POCKET-26B trades a little size for the thing people kept asking for — it just loads, everywhere, today: Ollama, LM Studio, PocketPal, MLX, any stock llama.cpp. No fork, no bleeding-edge runtime, no CUDA, no cloud.
It's a sparse Mixture-of-Experts (25.2B total, ~4B active per token), so the work per token stays small — a real 26B that generates on a CPU with no graphics card.
Two things make it stand out:
1) Universal compatibility. Gemma 4 is a standard, widely-supported architecture, so POCKET-26B runs on the tools you already have — no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
• Full base: 67.7%
• POCKET-26B Q4_K_M (17 GB): 67.7% — lossless
• POCKET-26B Q2_K (11 GB): 67.2% — near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space — POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B ≈ 19 tok/s vs Bonsai ≈ 6 tok/s → about 3× faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
• POCKET-35B (Qwen MoE) — bigger, top-tier, needs a recent llama.cpp.
• POCKET-26B (Gemma 4) — loads in any app, quality-robust when compressed. The demo runs the Q4_K_M build; Q2_K (11 GB) is the smallest footprint. For a true ≤8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
🖥️ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
📦 POCKET-26B-GGUF (Q4_K_M 17 GB · Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
📚 POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
We're adding a Gemma-4 sibling to POCKET: POCKET-26B, built from Google's Gemma-4-26B-A4B (Apache-2.0). Our flagship POCKET-35B is a Qwen-family MoE and needs a recent llama.cpp; POCKET-26B trades a little size for the thing people kept asking for — it just loads, everywhere, today: Ollama, LM Studio, PocketPal, MLX, any stock llama.cpp. No fork, no bleeding-edge runtime, no CUDA, no cloud.
It's a sparse Mixture-of-Experts (25.2B total, ~4B active per token), so the work per token stays small — a real 26B that generates on a CPU with no graphics card.
Two things make it stand out:
1) Universal compatibility. Gemma 4 is a standard, widely-supported architecture, so POCKET-26B runs on the tools you already have — no waiting for your app to add a new model type.
2) Quality that survives compression. Measured GPQA-Diamond (198 q, greedy):
• Full base: 67.7%
• POCKET-26B Q4_K_M (17 GB): 67.7% — lossless
• POCKET-26B Q2_K (11 GB): 67.2% — near-lossless, at 11 GB
Live, on a CPU-only box (our demo Space — POCKET-26B vs Bonsai-27B, same machine, same stock llama.cpp): POCKET-26B ≈ 19 tok/s vs Bonsai ≈ 6 tok/s → about 3× faster generation, no GPU. (Honest notes: shared CPU box, sequential race; a dedicated machine is faster.)
Where it fits in the family:
• POCKET-35B (Qwen MoE) — bigger, top-tier, needs a recent llama.cpp.
• POCKET-26B (Gemma 4) — loads in any app, quality-robust when compressed. The demo runs the Q4_K_M build; Q2_K (11 GB) is the smallest footprint. For a true ≤8 GB phone, the 5 GB POCKET-KR (Qwen) is still the pick.
Try it and grab it:
🖥️ Live demo (Gemma4-based, answering on a CPU, no GPU): FINAL-Bench/POCKET-26B-CPU
📦 POCKET-26B-GGUF (Q4_K_M 17 GB · Q2_K 11 GB): FINAL-Bench/POCKET-26B-GGUF
📚 POCKET collection: https://huggingface.co/collections/FINAL-Bench/pocket-models
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liked 12
models 7 days ago
FINAL-Bench/POCKET-26B-GGUF
Text Generation • 25B • Updated • 6.73k • 36
mradermacher/Darwin-V9-Chimera-4B-SFT-i1-GGUF
4B • Updated • 1.26k • 5
mradermacher/Darwin-V9-Chimera-4B-i1-GGUF
4B • Updated • 1.01k • 5
FINAL-Bench/Darwin-35B-A3B-Opus
Image-Text-to-Text • 36B • Updated • 169 • 95
FINAL-Bench/Darwin-9B-Opus
Text Generation • 10B • Updated • 125 • 39
FINAL-Bench/Darwin-31B-Opus
Text Generation • 33B • Updated • 160 • 57
FINAL-Bench/Darwin-4B-Genesis
Text Generation • 8B • Updated • 164 • 57
FINAL-Bench/Darwin-27B-Opus
Text Generation • 28B • Updated • 16 • 41
FINAL-Bench/Darwin-9B-NEG
Text Generation • 10B • Updated • 205 • 55
FINAL-Bench/Darwin-9B-MFP4
Text Generation • 7B • Updated • 167 • 21
FINAL-Bench/Darwin-28B-Coder
Text Generation • 27B • Updated • 156 • 32
FINAL-Bench/Darwin-60B-DUO
Text Generation • Updated • 48 • 41